This study is examining the association between DNA methylation and childhood eczema (up to ~10 years). Studies have data from different timepoints, so in the first instance we will classify individuals as ‘any eczema’ versus ‘no eczema’ according to all timepoints available up to age 10. Sub-analyses will be conducted that limits cases to those children who are diagnosed early in childhood (by ~2 years) or have persistent eczema (diagnosed by age 2 and have current eczema at age 8yrs).
The analyses will therefore use 3 binary definitions:
The association of each outcome with DNA methylation will be explored using logistic regression and 3 models with different covariates:
In this report, the correlation between cohort effect estimates is examined. Further the relationship between effect estimate correlation and difference between prevalence estimates is assessed.
After the summary below and under each eczema type and model there are tables that show pairwise comparisons of each cohort. The correlation of effect estimates across the top 30 CpGs (the 30 CpGs with the lowest P values in the meta-analysis of EWAS) are shown (under the “effect_cor” column heading) alongside the difference in prevalence estimates between the cohorts (under the “prev_diff” column heading).
Using the top 30 CpG sites from the meta-analysis for each model, M-statistics were also calculated. These statistics give an indication of the study-wide heterogeneity across CpG sites, rather than typical heterogeneity statistics (such as I2) that assess heterogeneity across studies for single CpG sites.
| eczema-definition | model | n-tophits | maxp | top-hit-wilc-p | all-wilc-p |
|---|---|---|---|---|---|
| childhood | a | 30 | 7.7e-05 | 1.8e-14 | 1.7e-14 |
| childhood | b | 30 | 5.7e-05 | 1.7e-14 | 1.7e-14 |
| childhood | c | 30 | 1.8e-04 | 1.9e-12 | 1.7e-12 |
| early-onset | a | 30 | 5.7e-05 | 1.4e-16 | 1.2e-16 |
| early-onset | b | 30 | 7.9e-05 | 1.4e-16 | 1.2e-16 |
| early-onset | c | 30 | 7.4e-05 | 1.4e-16 | 1.2e-16 |
| persistent | a | 30 | 7.9e-05 | 5.8e-11 | 2.9e-11 |
| persistent | b | 30 | 7.5e-05 | 5.8e-11 | 2.9e-11 |
| persistent | c | 30 | 7.9e-05 | 2.9e-11 | 2.9e-11 |
| cohort | N | N-cases | N-controls | prevalence |
|---|---|---|---|---|
| ALSPAC | 673 | 343 | 330 | 51.0 |
| CHS | 197 | 50 | 147 | 25.4 |
| EDEN | 153 | 69 | 84 | 45.1 |
| GENR | 450 | 69 | 381 | 15.3 |
| INMA | 462 | 216 | 246 | 46.8 |
| MoBa1 | 831 | 399 | 432 | 48.0 |
| MoBa2 | 418 | 179 | 239 | 42.8 |
| NEST_B | 77 | 56 | 21 | 72.7 |
| NEST_W | 41 | 22 | 19 | 53.7 |
| PREDO | 792 | 54 | 738 | 6.8 |
| VITO | 78 | 23 | 55 | 29.5 |
| GOYA | 517 | 93 | 424 | 18.0 |
| IOW | 616 | 179 | 437 | 29.1 |
| Total | 5,305 | 1,752 | 3,553 | 33.0 |
| model | variable | beta | se | zval | pval | lower-ci | upper-ci |
|---|---|---|---|---|---|---|---|
| m1a | N | -0.00051 | 0.00024 | -2.12 | 0.034 | -0.00098 | -3.9e-05 |
| m1a | N_cases | -0.00031 | 0.00064 | -0.49 | 0.628 | -0.00157 | 9.5e-04 |
| m1a | prevalence | 0.00824 | 0.00352 | 2.34 | 0.019 | 0.00134 | 1.5e-02 |
| m1a | definition | 0.15479 | 0.15367 | 1.01 | 0.314 | -0.14639 | 4.6e-01 |
| m1a | definition_3grp | 0.05426 | 0.15402 | 0.35 | 0.725 | -0.24761 | 3.6e-01 |
| m1b | N | -0.00043 | 0.00029 | -1.49 | 0.136 | -0.00099 | 1.3e-04 |
| m1b | N_cases | -0.00032 | 0.00072 | -0.44 | 0.656 | -0.00173 | 1.1e-03 |
| m1b | prevalence | 0.00745 | 0.00415 | 1.79 | 0.073 | -0.00069 | 1.6e-02 |
| m1b | definition | 0.14632 | 0.20392 | 0.72 | 0.473 | -0.25335 | 5.5e-01 |
| m1b | definition_3grp | 0.03536 | 0.21558 | 0.16 | 0.870 | -0.38717 | 4.6e-01 |
| m1c | N | -0.00004 | 0.00020 | -0.20 | 0.844 | -0.00044 | 3.6e-04 |
| m1c | N_cases | 0.00031 | 0.00044 | 0.70 | 0.482 | -0.00055 | 1.2e-03 |
| m1c | prevalence | 0.00223 | 0.00284 | 0.79 | 0.432 | -0.00333 | 7.8e-03 |
| m1c | definition | 0.24785 | 0.12568 | 1.97 | 0.049 | 0.00153 | 4.9e-01 |
| m1c | definition_3grp | 0.16596 | 0.12615 | 1.32 | 0.188 | -0.08129 | 4.1e-01 |
| cohort1 | cohort2 | effect-cor | prev-diff |
|---|---|---|---|
| ALSPAC | CHS | 0.724 | 25.59 |
| ALSPAC | EDEN | 0.518 | 5.87 |
| CHS | EDEN | 0.325 | 19.72 |
| ALSPAC | GENR | 0.700 | 35.63 |
| CHS | GENR | 0.506 | 10.05 |
| EDEN | GENR | 0.698 | 29.76 |
| ALSPAC | GOYA | 0.074 | 32.98 |
| CHS | GOYA | -0.241 | 7.39 |
| EDEN | GOYA | -0.147 | 27.11 |
| GENR | GOYA | -0.157 | 2.66 |
| ALSPAC | INMA | 0.687 | 4.21 |
| CHS | INMA | 0.567 | 21.37 |
| EDEN | INMA | 0.597 | 1.66 |
| GENR | INMA | 0.779 | 31.42 |
| GOYA | INMA | -0.078 | 28.76 |
| ALSPAC | IOW | 0.761 | 21.91 |
| CHS | IOW | 0.564 | 3.68 |
| EDEN | IOW | 0.575 | 16.04 |
| GENR | IOW | 0.753 | 13.73 |
| GOYA | IOW | -0.153 | 11.07 |
| INMA | IOW | 0.855 | 17.69 |
| ALSPAC | Meta | 0.914 | NA |
| CHS | Meta | 0.711 | NA |
| EDEN | Meta | 0.634 | NA |
| GENR | Meta | 0.843 | NA |
| GOYA | Meta | -0.013 | NA |
| INMA | Meta | 0.880 | NA |
| IOW | Meta | 0.908 | NA |
| ALSPAC | MoBa1 | 0.708 | 2.95 |
| CHS | MoBa1 | 0.509 | 22.63 |
| EDEN | MoBa1 | 0.601 | 2.92 |
| GENR | MoBa1 | 0.765 | 32.68 |
| GOYA | MoBa1 | -0.062 | 30.03 |
| INMA | MoBa1 | 0.803 | 1.26 |
| IOW | MoBa1 | 0.837 | 18.96 |
| Meta | MoBa1 | 0.865 | NA |
| ALSPAC | MoBa2 | 0.407 | 8.14 |
| CHS | MoBa2 | 0.258 | 17.44 |
| EDEN | MoBa2 | 0.482 | 2.28 |
| GENR | MoBa2 | 0.490 | 27.49 |
| GOYA | MoBa2 | 0.131 | 24.83 |
| INMA | MoBa2 | 0.253 | 3.93 |
| IOW | MoBa2 | 0.298 | 13.76 |
| Meta | MoBa2 | 0.460 | NA |
| MoBa1 | MoBa2 | 0.284 | 5.19 |
| ALSPAC | NEST_B | 0.714 | 21.76 |
| CHS | NEST_B | 0.551 | 47.35 |
| EDEN | NEST_B | 0.677 | 27.63 |
| GENR | NEST_B | 0.713 | 57.39 |
| GOYA | NEST_B | -0.121 | 54.74 |
| INMA | NEST_B | 0.831 | 25.97 |
| IOW | NEST_B | 0.833 | 43.67 |
| Meta | NEST_B | 0.845 | NA |
| MoBa1 | NEST_B | 0.809 | 24.71 |
| MoBa2 | NEST_B | 0.201 | 29.90 |
| ALSPAC | NEST_W | 0.041 | 2.69 |
| CHS | NEST_W | -0.315 | 28.28 |
| EDEN | NEST_W | 0.421 | 8.56 |
| GENR | NEST_W | 0.396 | 38.33 |
| GOYA | NEST_W | 0.241 | 35.67 |
| INMA | NEST_W | 0.417 | 6.91 |
| IOW | NEST_W | 0.328 | 24.60 |
| Meta | NEST_W | 0.260 | NA |
| MoBa1 | NEST_W | 0.373 | 5.64 |
| MoBa2 | NEST_W | 0.204 | 10.84 |
| NEST_B | NEST_W | 0.336 | 19.07 |
| ALSPAC | PREDO | 0.793 | 44.15 |
| CHS | PREDO | 0.622 | 18.56 |
| EDEN | PREDO | 0.578 | 38.28 |
| GENR | PREDO | 0.786 | 8.52 |
| GOYA | PREDO | 0.083 | 11.17 |
| INMA | PREDO | 0.713 | 39.94 |
| IOW | PREDO | 0.729 | 22.24 |
| Meta | PREDO | 0.865 | NA |
| MoBa1 | PREDO | 0.628 | 41.20 |
| MoBa2 | PREDO | 0.562 | 36.00 |
| NEST_B | PREDO | 0.681 | 65.91 |
| NEST_W | PREDO | 0.248 | 46.84 |
| ALSPAC | VITO | 0.580 | 21.48 |
| CHS | VITO | 0.587 | 4.11 |
| EDEN | VITO | 0.121 | 15.61 |
| GENR | VITO | 0.505 | 14.15 |
| GOYA | VITO | -0.075 | 11.50 |
| INMA | VITO | 0.549 | 17.27 |
| IOW | VITO | 0.500 | 0.43 |
| Meta | VITO | 0.607 | NA |
| MoBa1 | VITO | 0.404 | 18.53 |
| MoBa2 | VITO | 0.176 | 13.34 |
| NEST_B | VITO | 0.459 | 43.24 |
| NEST_W | VITO | -0.104 | 24.17 |
| PREDO | VITO | 0.491 | 22.67 |
Figure 1: Correlation between the effect estimates of childhood eczema EWAS, model a, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.
Figure 2: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.
Figure 3: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.
| cohort1 | cohort2 | effect-cor | prev-diff |
|---|---|---|---|
| ALSPAC | CHS | 0.868 | 25.59 |
| ALSPAC | EDEN | 0.452 | 5.87 |
| CHS | EDEN | 0.256 | 19.72 |
| ALSPAC | GENR | 0.783 | 35.63 |
| CHS | GENR | 0.706 | 10.05 |
| EDEN | GENR | 0.559 | 29.76 |
| ALSPAC | GOYA | 0.504 | 32.98 |
| CHS | GOYA | 0.392 | 7.39 |
| EDEN | GOYA | 0.126 | 27.11 |
| GENR | GOYA | 0.243 | 2.66 |
| ALSPAC | INMA | 0.476 | 3.60 |
| CHS | INMA | 0.466 | 21.99 |
| EDEN | INMA | 0.412 | 2.27 |
| GENR | INMA | 0.371 | 32.04 |
| GOYA | INMA | 0.311 | 29.38 |
| ALSPAC | IOW | 0.659 | 21.71 |
| CHS | IOW | 0.626 | 3.88 |
| EDEN | IOW | 0.487 | 15.84 |
| GENR | IOW | 0.594 | 13.92 |
| GOYA | IOW | 0.205 | 11.27 |
| INMA | IOW | 0.601 | 18.11 |
| ALSPAC | Meta | 0.936 | NA |
| CHS | Meta | 0.862 | NA |
| EDEN | Meta | 0.564 | NA |
| GENR | Meta | 0.811 | NA |
| GOYA | Meta | 0.462 | NA |
| INMA | Meta | 0.675 | NA |
| IOW | Meta | 0.784 | NA |
| ALSPAC | MoBa1 | 0.745 | 2.90 |
| CHS | MoBa1 | 0.752 | 22.69 |
| EDEN | MoBa1 | 0.394 | 2.97 |
| GENR | MoBa1 | 0.619 | 32.73 |
| GOYA | MoBa1 | 0.188 | 30.08 |
| INMA | MoBa1 | 0.494 | 0.70 |
| IOW | MoBa1 | 0.623 | 18.81 |
| Meta | MoBa1 | 0.807 | NA |
| ALSPAC | MoBa2 | 0.726 | 8.14 |
| CHS | MoBa2 | 0.572 | 17.44 |
| EDEN | MoBa2 | 0.727 | 2.28 |
| GENR | MoBa2 | 0.757 | 27.49 |
| GOYA | MoBa2 | 0.301 | 24.83 |
| INMA | MoBa2 | 0.639 | 4.55 |
| IOW | MoBa2 | 0.695 | 13.57 |
| Meta | MoBa2 | 0.849 | NA |
| MoBa1 | MoBa2 | 0.653 | 5.24 |
| ALSPAC | NEST_B | 0.518 | 21.76 |
| CHS | NEST_B | 0.497 | 47.35 |
| EDEN | NEST_B | 0.482 | 27.63 |
| GENR | NEST_B | 0.442 | 57.39 |
| GOYA | NEST_B | 0.253 | 54.74 |
| INMA | NEST_B | 0.512 | 25.36 |
| IOW | NEST_B | 0.549 | 43.47 |
| Meta | NEST_B | 0.640 | NA |
| MoBa1 | NEST_B | 0.500 | 24.66 |
| MoBa2 | NEST_B | 0.494 | 29.90 |
| ALSPAC | NEST_W | 0.078 | 2.69 |
| CHS | NEST_W | 0.031 | 28.28 |
| EDEN | NEST_W | 0.508 | 8.56 |
| GENR | NEST_W | 0.134 | 38.33 |
| GOYA | NEST_W | 0.067 | 35.67 |
| INMA | NEST_W | 0.378 | 6.29 |
| IOW | NEST_W | 0.197 | 24.40 |
| Meta | NEST_W | 0.206 | NA |
| MoBa1 | NEST_W | 0.024 | 5.59 |
| MoBa2 | NEST_W | 0.396 | 10.84 |
| NEST_B | NEST_W | 0.237 | 19.07 |
| ALSPAC | PREDO | 0.833 | 44.15 |
| CHS | PREDO | 0.775 | 18.56 |
| EDEN | PREDO | 0.488 | 38.28 |
| GENR | PREDO | 0.759 | 8.52 |
| GOYA | PREDO | 0.383 | 11.17 |
| INMA | PREDO | 0.509 | 40.55 |
| IOW | PREDO | 0.673 | 22.44 |
| Meta | PREDO | 0.883 | NA |
| MoBa1 | PREDO | 0.600 | 41.25 |
| MoBa2 | PREDO | 0.726 | 36.00 |
| NEST_B | PREDO | 0.596 | 65.91 |
| NEST_W | PREDO | 0.215 | 46.84 |
| ALSPAC | VITO | 0.592 | 21.48 |
| CHS | VITO | 0.484 | 4.11 |
| EDEN | VITO | 0.117 | 15.61 |
| GENR | VITO | 0.598 | 14.15 |
| GOYA | VITO | 0.523 | 11.50 |
| INMA | VITO | 0.146 | 17.88 |
| IOW | VITO | 0.216 | 0.23 |
| Meta | VITO | 0.523 | NA |
| MoBa1 | VITO | 0.291 | 18.58 |
| MoBa2 | VITO | 0.433 | 13.34 |
| NEST_B | VITO | 0.173 | 43.24 |
| NEST_W | VITO | -0.127 | 24.17 |
| PREDO | VITO | 0.452 | 22.67 |
Figure 4: Correlation between the effect estimates of childhood eczema EWAS, model b, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.
Figure 5: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.
Figure 6: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.
| cohort1 | cohort2 | effect-cor | prev-diff |
|---|---|---|---|
| ALSPAC | CHS | 0.62715 | 25.59 |
| ALSPAC | EDEN | 0.35254 | 5.87 |
| CHS | EDEN | 0.42439 | 19.72 |
| ALSPAC | GENR | 0.40856 | 35.63 |
| CHS | GENR | 0.51558 | 10.05 |
| EDEN | GENR | 0.55747 | 29.76 |
| ALSPAC | GOYA | 0.63175 | 32.98 |
| CHS | GOYA | 0.29616 | 7.39 |
| EDEN | GOYA | 0.18751 | 27.11 |
| GENR | GOYA | 0.21614 | 2.66 |
| ALSPAC | INMA | 0.57363 | 3.60 |
| CHS | INMA | 0.57320 | 21.99 |
| EDEN | INMA | 0.48464 | 2.27 |
| GENR | INMA | 0.40610 | 32.04 |
| GOYA | INMA | 0.37862 | 29.38 |
| ALSPAC | IOW | 0.71055 | 21.71 |
| CHS | IOW | 0.70628 | 3.88 |
| EDEN | IOW | 0.36014 | 15.84 |
| GENR | IOW | 0.33691 | 13.92 |
| GOYA | IOW | 0.41357 | 11.27 |
| INMA | IOW | 0.77705 | 18.11 |
| ALSPAC | Meta | 0.87786 | NA |
| CHS | Meta | 0.76469 | NA |
| EDEN | Meta | 0.52273 | NA |
| GENR | Meta | 0.61841 | NA |
| GOYA | Meta | 0.57536 | NA |
| INMA | Meta | 0.80060 | NA |
| IOW | Meta | 0.85849 | NA |
| ALSPAC | MoBa1 | 0.66705 | 2.90 |
| CHS | MoBa1 | 0.65597 | 22.69 |
| EDEN | MoBa1 | 0.46307 | 2.97 |
| GENR | MoBa1 | 0.63112 | 32.73 |
| GOYA | MoBa1 | 0.42647 | 30.08 |
| INMA | MoBa1 | 0.79494 | 0.70 |
| IOW | MoBa1 | 0.70355 | 18.81 |
| Meta | MoBa1 | 0.87096 | NA |
| ALSPAC | MoBa2 | 0.67930 | 8.14 |
| CHS | MoBa2 | 0.48053 | 17.44 |
| EDEN | MoBa2 | 0.61650 | 2.28 |
| GENR | MoBa2 | 0.72558 | 27.49 |
| GOYA | MoBa2 | 0.52847 | 24.83 |
| INMA | MoBa2 | 0.48847 | 4.55 |
| IOW | MoBa2 | 0.55732 | 13.57 |
| Meta | MoBa2 | 0.74569 | NA |
| MoBa1 | MoBa2 | 0.60287 | 5.24 |
| ALSPAC | NEST_B | 0.03950 | 21.76 |
| CHS | NEST_B | -0.04220 | 47.35 |
| EDEN | NEST_B | 0.30469 | 27.63 |
| GENR | NEST_B | 0.09003 | 57.39 |
| GOYA | NEST_B | -0.00019 | 54.74 |
| INMA | NEST_B | -0.05804 | 25.36 |
| IOW | NEST_B | -0.22943 | 43.47 |
| Meta | NEST_B | 0.01553 | NA |
| MoBa1 | NEST_B | -0.03350 | 24.66 |
| MoBa2 | NEST_B | -0.05051 | 29.90 |
| ALSPAC | PREDO | 0.78875 | 44.15 |
| CHS | PREDO | 0.73160 | 18.56 |
| EDEN | PREDO | 0.35564 | 38.28 |
| GENR | PREDO | 0.58113 | 8.52 |
| GOYA | PREDO | 0.46070 | 11.17 |
| INMA | PREDO | 0.61208 | 40.55 |
| IOW | PREDO | 0.69849 | 22.44 |
| Meta | PREDO | 0.86363 | NA |
| MoBa1 | PREDO | 0.69942 | 41.25 |
| MoBa2 | PREDO | 0.58478 | 36.00 |
| NEST_B | PREDO | 0.15223 | 65.91 |
| ALSPAC | VITO | 0.23321 | 21.48 |
| CHS | VITO | 0.44731 | 4.11 |
| EDEN | VITO | 0.29879 | 15.61 |
| GENR | VITO | 0.01956 | 14.15 |
| GOYA | VITO | 0.13552 | 11.50 |
| INMA | VITO | 0.30730 | 17.88 |
| IOW | VITO | 0.33803 | 0.23 |
| Meta | VITO | 0.34064 | NA |
| MoBa1 | VITO | 0.24834 | 18.58 |
| MoBa2 | VITO | 0.18651 | 13.34 |
| NEST_B | VITO | 0.05068 | 43.24 |
| PREDO | VITO | 0.21045 | 22.67 |
Figure 7: Correlation between the effect estimates of childhood eczema EWAS, model c, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.
Figure 8: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.
Figure 9: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.
| cohort | N | N-cases | N-controls | prevalence |
|---|---|---|---|---|
| ALSPAC | 667 | 169 | 498 | 25.3 |
| CHS | 197 | 41 | 156 | 20.8 |
| DCHS | 266 | 26 | 240 | 9.8 |
| EDEN | 142 | 48 | 94 | 33.8 |
| GENR | 742 | 171 | 571 | 23.0 |
| INMA | 348 | 83 | 265 | 23.9 |
| IOW-F2 | 171 | 28 | 143 | 16.4 |
| MoBa1 | 993 | 361 | 632 | 36.4 |
| MoBa2 | 386 | 210 | 176 | 54.4 |
| NEST_B | 119 | 30 | 89 | 25.2 |
| UKIDS | 607 | 216 | 390 | 35.6 |
| VITO | 70 | 15 | 55 | 21.4 |
| GOYA | 517 | 39 | 478 | 7.5 |
| IOW | 667 | 99 | 569 | 14.8 |
| Total | 5,892 | 1,536 | 4,356 | 26.1 |
| model | variable | beta | se | zval | pval | lower-ci | upper-ci |
|---|---|---|---|---|---|---|---|
| m2a | N | -3.3e-04 | 0.00031 | -1.06 | 0.287 | -0.00094 | 2.8e-04 |
| m2a | N_cases | -1.2e-03 | 0.00083 | -1.41 | 0.159 | -0.00279 | 4.6e-04 |
| m2a | prevalence | -7.9e-03 | 0.00711 | -1.12 | 0.264 | -0.02188 | 6.0e-03 |
| m2a | definition | 5.3e-02 | 0.17708 | 0.30 | 0.767 | -0.29456 | 4.0e-01 |
| m2a | definition_3grp | 9.5e-02 | 0.20515 | 0.46 | 0.644 | -0.30739 | 5.0e-01 |
| m2b | N | -2.2e-04 | 0.00034 | -0.64 | 0.520 | -0.00088 | 4.4e-04 |
| m2b | N_cases | -4.4e-04 | 0.00093 | -0.47 | 0.638 | -0.00226 | 1.4e-03 |
| m2b | prevalence | -1.4e-03 | 0.00784 | -0.18 | 0.857 | -0.01678 | 1.4e-02 |
| m2b | definition | -7.9e-02 | 0.20903 | -0.38 | 0.705 | -0.48890 | 3.3e-01 |
| m2b | definition_3grp | -8.1e-02 | 0.24473 | -0.33 | 0.739 | -0.56115 | 4.0e-01 |
| m2c | N | -2.0e-04 | 0.00015 | -1.35 | 0.176 | -0.00050 | 9.1e-05 |
| m2c | N_cases | -7.7e-05 | 0.00044 | -0.18 | 0.861 | -0.00094 | 7.9e-04 |
| m2c | prevalence | 5.9e-03 | 0.00328 | 1.79 | 0.074 | -0.00056 | 1.2e-02 |
| m2c | definition | -3.3e-02 | 0.09279 | -0.35 | 0.723 | -0.21473 | 1.5e-01 |
| m2c | definition_3grp | -1.2e-01 | 0.09152 | -1.26 | 0.208 | -0.29451 | 6.4e-02 |
| cohort1 | cohort2 | effect-cor | prev-diff |
|---|---|---|---|
| ALSPAC | CHS | 0.72 | 4.53 |
| ALSPAC | DCHS | 0.37 | 15.56 |
| CHS | DCHS | 0.31 | 11.04 |
| ALSPAC | EDEN | 0.74 | 8.47 |
| CHS | EDEN | 0.49 | 12.99 |
| DCHS | EDEN | 0.38 | 24.03 |
| ALSPAC | GENR | 0.56 | 2.29 |
| CHS | GENR | 0.38 | 2.23 |
| DCHS | GENR | 0.19 | 13.27 |
| EDEN | GENR | 0.48 | 10.76 |
| ALSPAC | GOYA | 0.56 | 17.79 |
| CHS | GOYA | 0.36 | 13.27 |
| DCHS | GOYA | 0.34 | 2.23 |
| EDEN | GOYA | 0.42 | 26.26 |
| GENR | GOYA | 0.62 | 15.50 |
| ALSPAC | INMA | 0.80 | 1.49 |
| CHS | INMA | 0.50 | 3.04 |
| DCHS | INMA | 0.49 | 14.08 |
| EDEN | INMA | 0.65 | 9.95 |
| GENR | INMA | 0.61 | 0.80 |
| GOYA | INMA | 0.76 | 16.31 |
| ALSPAC | IOW | 0.71 | 10.49 |
| CHS | IOW | 0.60 | 5.97 |
| DCHS | IOW | 0.58 | 5.07 |
| EDEN | IOW | 0.62 | 18.96 |
| GENR | IOW | 0.37 | 8.20 |
| GOYA | IOW | 0.23 | 7.30 |
| INMA | IOW | 0.62 | 9.01 |
| ALSPAC | IOW-F2 | 0.64 | 8.96 |
| CHS | IOW-F2 | 0.48 | 4.44 |
| DCHS | IOW-F2 | 0.24 | 6.60 |
| EDEN | IOW-F2 | 0.41 | 17.43 |
| GENR | IOW-F2 | 0.33 | 6.67 |
| GOYA | IOW-F2 | 0.36 | 8.83 |
| INMA | IOW-F2 | 0.53 | 7.48 |
| IOW | IOW-F2 | 0.57 | 1.53 |
| ALSPAC | Meta | 0.95 | NA |
| CHS | Meta | 0.71 | NA |
| DCHS | Meta | 0.49 | NA |
| EDEN | Meta | 0.77 | NA |
| GENR | Meta | 0.60 | NA |
| GOYA | Meta | 0.64 | NA |
| INMA | Meta | 0.91 | NA |
| IOW | Meta | 0.75 | NA |
| IOW-F2 | Meta | 0.63 | NA |
| ALSPAC | MoBa1 | 0.64 | 11.02 |
| CHS | MoBa1 | 0.41 | 15.54 |
| DCHS | MoBa1 | 0.45 | 26.58 |
| EDEN | MoBa1 | 0.67 | 2.55 |
| GENR | MoBa1 | 0.49 | 13.31 |
| GOYA | MoBa1 | 0.63 | 28.81 |
| INMA | MoBa1 | 0.86 | 12.50 |
| IOW | MoBa1 | 0.55 | 21.51 |
| IOW-F2 | MoBa1 | 0.44 | 19.98 |
| Meta | MoBa1 | 0.82 | NA |
| ALSPAC | MoBa2 | 0.57 | 29.07 |
| CHS | MoBa2 | 0.47 | 33.59 |
| DCHS | MoBa2 | 0.71 | 44.63 |
| EDEN | MoBa2 | 0.65 | 20.60 |
| GENR | MoBa2 | 0.13 | 31.36 |
| GOYA | MoBa2 | 0.31 | 46.86 |
| INMA | MoBa2 | 0.49 | 30.55 |
| IOW | MoBa2 | 0.65 | 39.56 |
| IOW-F2 | MoBa2 | 0.53 | 38.03 |
| Meta | MoBa2 | 0.63 | NA |
| MoBa1 | MoBa2 | 0.54 | 18.05 |
| ALSPAC | NEST_B | 0.73 | 0.13 |
| CHS | NEST_B | 0.59 | 4.40 |
| DCHS | NEST_B | 0.42 | 15.44 |
| EDEN | NEST_B | 0.61 | 8.59 |
| GENR | NEST_B | 0.50 | 2.16 |
| GOYA | NEST_B | 0.70 | 17.67 |
| INMA | NEST_B | 0.83 | 1.36 |
| IOW | NEST_B | 0.52 | 10.37 |
| IOW-F2 | NEST_B | 0.37 | 8.84 |
| Meta | NEST_B | 0.84 | NA |
| MoBa1 | NEST_B | 0.82 | 11.14 |
| MoBa2 | NEST_B | 0.43 | 29.19 |
| ALSPAC | UKIDS | 0.91 | 10.25 |
| CHS | UKIDS | 0.63 | 14.77 |
| DCHS | UKIDS | 0.21 | 25.81 |
| EDEN | UKIDS | 0.55 | 1.78 |
| GENR | UKIDS | 0.48 | 12.54 |
| GOYA | UKIDS | 0.59 | 28.04 |
| INMA | UKIDS | 0.79 | 11.73 |
| IOW | UKIDS | 0.53 | 20.74 |
| IOW-F2 | UKIDS | 0.62 | 19.21 |
| Meta | UKIDS | 0.88 | NA |
| MoBa1 | UKIDS | 0.65 | 0.77 |
| MoBa2 | UKIDS | 0.37 | 18.82 |
| NEST_B | UKIDS | 0.72 | 10.37 |
| ALSPAC | VITO | 0.60 | 3.91 |
| CHS | VITO | 0.38 | 0.62 |
| DCHS | VITO | 0.34 | 11.65 |
| EDEN | VITO | 0.45 | 12.37 |
| GENR | VITO | 0.10 | 1.62 |
| GOYA | VITO | 0.43 | 13.89 |
| INMA | VITO | 0.60 | 2.42 |
| IOW | VITO | 0.49 | 6.59 |
| IOW-F2 | VITO | 0.33 | 5.05 |
| Meta | VITO | 0.61 | NA |
| MoBa1 | VITO | 0.55 | 14.93 |
| MoBa2 | VITO | 0.53 | 32.98 |
| NEST_B | VITO | 0.54 | 3.78 |
| UKIDS | VITO | 0.51 | 14.16 |
Figure 10: Correlation between the effect estimates of early-onset eczema EWAS, model a, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.
Figure 11: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.
Figure 12: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.
| cohort1 | cohort2 | effect-cor | prev-diff |
|---|---|---|---|
| ALSPAC | CHS | 0.464 | 4.53 |
| ALSPAC | DCHS | -0.011 | 15.90 |
| CHS | DCHS | 0.369 | 11.38 |
| ALSPAC | EDEN | 0.514 | 8.47 |
| CHS | EDEN | 0.581 | 12.99 |
| DCHS | EDEN | 0.323 | 24.37 |
| ALSPAC | GENR | 0.673 | 2.29 |
| CHS | GENR | 0.359 | 2.23 |
| DCHS | GENR | -0.035 | 13.61 |
| EDEN | GENR | 0.563 | 10.76 |
| ALSPAC | GOYA | 0.063 | 17.79 |
| CHS | GOYA | 0.344 | 13.27 |
| DCHS | GOYA | 0.524 | 1.89 |
| EDEN | GOYA | 0.039 | 26.26 |
| GENR | GOYA | 0.024 | 15.50 |
| ALSPAC | INMA | 0.775 | 1.79 |
| CHS | INMA | 0.528 | 2.73 |
| DCHS | INMA | 0.384 | 14.11 |
| EDEN | INMA | 0.631 | 10.26 |
| GENR | INMA | 0.658 | 0.50 |
| GOYA | INMA | 0.211 | 16.00 |
| ALSPAC | IOW | 0.406 | 10.28 |
| CHS | IOW | 0.234 | 5.75 |
| DCHS | IOW | -0.034 | 5.63 |
| EDEN | IOW | 0.169 | 18.74 |
| GENR | IOW | 0.189 | 7.98 |
| GOYA | IOW | -0.099 | 7.52 |
| INMA | IOW | 0.489 | 8.48 |
| ALSPAC | IOW-F2 | 0.571 | 8.88 |
| CHS | IOW-F2 | 0.057 | 4.36 |
| DCHS | IOW-F2 | -0.041 | 7.02 |
| EDEN | IOW-F2 | 0.102 | 17.35 |
| GENR | IOW-F2 | 0.355 | 6.59 |
| GOYA | IOW-F2 | -0.037 | 8.91 |
| INMA | IOW-F2 | 0.493 | 7.09 |
| IOW | IOW-F2 | 0.438 | 1.39 |
| ALSPAC | Meta | 0.955 | NA |
| CHS | Meta | 0.552 | NA |
| DCHS | Meta | 0.176 | NA |
| EDEN | Meta | 0.566 | NA |
| GENR | Meta | 0.665 | NA |
| GOYA | Meta | 0.139 | NA |
| INMA | Meta | 0.882 | NA |
| IOW | Meta | 0.536 | NA |
| IOW-F2 | Meta | 0.580 | NA |
| ALSPAC | MoBa1 | 0.791 | 11.10 |
| CHS | MoBa1 | 0.423 | 15.63 |
| DCHS | MoBa1 | 0.011 | 27.00 |
| EDEN | MoBa1 | 0.273 | 2.63 |
| GENR | MoBa1 | 0.529 | 13.39 |
| GOYA | MoBa1 | -0.008 | 28.89 |
| INMA | MoBa1 | 0.579 | 12.89 |
| IOW | MoBa1 | 0.415 | 21.38 |
| IOW-F2 | MoBa1 | 0.424 | 19.98 |
| Meta | MoBa1 | 0.797 | NA |
| ALSPAC | MoBa2 | 0.684 | 29.07 |
| CHS | MoBa2 | 0.327 | 33.59 |
| DCHS | MoBa2 | 0.082 | 44.97 |
| EDEN | MoBa2 | 0.298 | 20.60 |
| GENR | MoBa2 | 0.319 | 31.36 |
| GOYA | MoBa2 | -0.200 | 46.86 |
| INMA | MoBa2 | 0.503 | 30.86 |
| IOW | MoBa2 | 0.523 | 39.34 |
| IOW-F2 | MoBa2 | 0.439 | 37.95 |
| Meta | MoBa2 | 0.702 | NA |
| MoBa1 | MoBa2 | 0.708 | 17.97 |
| ALSPAC | NEST_B | 0.547 | 0.13 |
| CHS | NEST_B | 0.353 | 4.40 |
| DCHS | NEST_B | 0.281 | 15.78 |
| EDEN | NEST_B | 0.208 | 8.59 |
| GENR | NEST_B | 0.173 | 2.16 |
| GOYA | NEST_B | 0.289 | 17.67 |
| INMA | NEST_B | 0.649 | 1.66 |
| IOW | NEST_B | 0.527 | 10.15 |
| IOW-F2 | NEST_B | 0.443 | 8.75 |
| Meta | NEST_B | 0.642 | NA |
| MoBa1 | NEST_B | 0.448 | 11.23 |
| MoBa2 | NEST_B | 0.350 | 29.19 |
| ALSPAC | UKIDS | 0.871 | 10.25 |
| CHS | UKIDS | 0.540 | 14.77 |
| DCHS | UKIDS | 0.077 | 26.15 |
| EDEN | UKIDS | 0.447 | 1.78 |
| GENR | UKIDS | 0.523 | 12.54 |
| GOYA | UKIDS | 0.202 | 28.04 |
| INMA | UKIDS | 0.809 | 12.04 |
| IOW | UKIDS | 0.471 | 20.52 |
| IOW-F2 | UKIDS | 0.508 | 19.13 |
| Meta | UKIDS | 0.899 | NA |
| MoBa1 | UKIDS | 0.637 | 0.85 |
| MoBa2 | UKIDS | 0.539 | 18.82 |
| NEST_B | UKIDS | 0.601 | 10.37 |
| ALSPAC | VITO | 0.479 | 3.91 |
| CHS | VITO | 0.038 | 0.62 |
| DCHS | VITO | -0.470 | 11.99 |
| EDEN | VITO | 0.197 | 12.37 |
| GENR | VITO | 0.252 | 1.62 |
| GOYA | VITO | -0.291 | 13.89 |
| INMA | VITO | 0.048 | 2.12 |
| IOW | VITO | -0.093 | 6.37 |
| IOW-F2 | VITO | 0.115 | 4.97 |
| Meta | VITO | 0.299 | NA |
| MoBa1 | VITO | 0.478 | 15.01 |
| MoBa2 | VITO | 0.283 | 32.98 |
| NEST_B | VITO | -0.053 | 3.78 |
| UKIDS | VITO | 0.208 | 14.16 |
Figure 13: Correlation between the effect estimates of early-onset eczema EWAS, model b, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.
Figure 14: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.
Figure 15: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.
| cohort1 | cohort2 | effect-cor | prev-diff |
|---|---|---|---|
| ALSPAC | CHS | 0.5173 | 4.53 |
| ALSPAC | DCHS | 0.0607 | 15.90 |
| CHS | DCHS | 0.4605 | 11.38 |
| ALSPAC | EDEN | 0.5178 | 8.47 |
| CHS | EDEN | 0.5985 | 12.99 |
| DCHS | EDEN | 0.2443 | 24.37 |
| ALSPAC | GENR | 0.5021 | 2.29 |
| CHS | GENR | 0.6697 | 2.23 |
| DCHS | GENR | 0.1961 | 13.61 |
| EDEN | GENR | 0.6859 | 10.76 |
| ALSPAC | GOYA | 0.3591 | 17.79 |
| CHS | GOYA | 0.2074 | 13.27 |
| DCHS | GOYA | 0.0931 | 1.89 |
| EDEN | GOYA | -0.0086 | 26.26 |
| GENR | GOYA | 0.1570 | 15.50 |
| ALSPAC | INMA | 0.5337 | 1.79 |
| CHS | INMA | 0.7530 | 2.73 |
| DCHS | INMA | 0.3702 | 14.11 |
| EDEN | INMA | 0.6319 | 10.26 |
| GENR | INMA | 0.8378 | 0.50 |
| GOYA | INMA | 0.0266 | 16.00 |
| ALSPAC | IOW | 0.6093 | 10.28 |
| CHS | IOW | 0.5160 | 5.75 |
| DCHS | IOW | 0.0437 | 5.63 |
| EDEN | IOW | 0.5477 | 18.74 |
| GENR | IOW | 0.5974 | 7.98 |
| GOYA | IOW | 0.1333 | 7.52 |
| INMA | IOW | 0.7487 | 8.48 |
| ALSPAC | IOW-F2 | 0.2976 | 8.88 |
| CHS | IOW-F2 | 0.4564 | 4.36 |
| DCHS | IOW-F2 | 0.1521 | 7.02 |
| EDEN | IOW-F2 | 0.5387 | 17.35 |
| GENR | IOW-F2 | 0.5272 | 6.59 |
| GOYA | IOW-F2 | -0.2518 | 8.91 |
| INMA | IOW-F2 | 0.5156 | 7.09 |
| IOW | IOW-F2 | 0.3806 | 1.39 |
| ALSPAC | Meta | 0.8016 | NA |
| CHS | Meta | 0.7673 | NA |
| DCHS | Meta | 0.2351 | NA |
| EDEN | Meta | 0.7356 | NA |
| GENR | Meta | 0.8446 | NA |
| GOYA | Meta | 0.2361 | NA |
| INMA | Meta | 0.8722 | NA |
| IOW | Meta | 0.7661 | NA |
| IOW-F2 | Meta | 0.5236 | NA |
| ALSPAC | MoBa1 | 0.5626 | 11.10 |
| CHS | MoBa1 | 0.6755 | 15.63 |
| DCHS | MoBa1 | 0.3529 | 27.00 |
| EDEN | MoBa1 | 0.4900 | 2.63 |
| GENR | MoBa1 | 0.6470 | 13.39 |
| GOYA | MoBa1 | 0.3150 | 28.89 |
| INMA | MoBa1 | 0.7054 | 12.89 |
| IOW | MoBa1 | 0.6422 | 21.38 |
| IOW-F2 | MoBa1 | 0.2879 | 19.98 |
| Meta | MoBa1 | 0.8110 | NA |
| ALSPAC | MoBa2 | 0.5196 | 29.07 |
| CHS | MoBa2 | 0.2449 | 33.59 |
| DCHS | MoBa2 | 0.0737 | 44.97 |
| EDEN | MoBa2 | 0.3981 | 20.60 |
| GENR | MoBa2 | 0.2523 | 31.36 |
| GOYA | MoBa2 | 0.2560 | 46.86 |
| INMA | MoBa2 | 0.2476 | 30.86 |
| IOW | MoBa2 | 0.5140 | 39.34 |
| IOW-F2 | MoBa2 | 0.2832 | 37.95 |
| Meta | MoBa2 | 0.4610 | NA |
| MoBa1 | MoBa2 | 0.2624 | 17.97 |
| ALSPAC | NEST_B | 0.5412 | 0.13 |
| CHS | NEST_B | 0.5905 | 4.40 |
| DCHS | NEST_B | 0.2607 | 15.78 |
| EDEN | NEST_B | 0.4961 | 8.59 |
| GENR | NEST_B | 0.5697 | 2.16 |
| GOYA | NEST_B | 0.1642 | 17.67 |
| INMA | NEST_B | 0.6139 | 1.66 |
| IOW | NEST_B | 0.3710 | 10.15 |
| IOW-F2 | NEST_B | 0.1080 | 8.75 |
| Meta | NEST_B | 0.6942 | NA |
| MoBa1 | NEST_B | 0.5343 | 11.23 |
| MoBa2 | NEST_B | 0.1217 | 29.19 |
| ALSPAC | UKIDS | 0.7279 | 10.25 |
| CHS | UKIDS | 0.7402 | 14.77 |
| DCHS | UKIDS | 0.1058 | 26.15 |
| EDEN | UKIDS | 0.5831 | 1.78 |
| GENR | UKIDS | 0.7375 | 12.54 |
| GOYA | UKIDS | 0.3411 | 28.04 |
| INMA | UKIDS | 0.7111 | 12.04 |
| IOW | UKIDS | 0.5899 | 20.52 |
| IOW-F2 | UKIDS | 0.4299 | 19.13 |
| Meta | UKIDS | 0.8861 | NA |
| MoBa1 | UKIDS | 0.6560 | 0.85 |
| MoBa2 | UKIDS | 0.2739 | 18.82 |
| NEST_B | UKIDS | 0.7121 | 10.37 |
| ALSPAC | VITO | 0.3307 | 3.91 |
| CHS | VITO | -0.2391 | 0.62 |
| DCHS | VITO | -0.4712 | 11.99 |
| EDEN | VITO | 0.1425 | 12.37 |
| GENR | VITO | 0.0423 | 1.62 |
| GOYA | VITO | 0.1611 | 13.89 |
| INMA | VITO | -0.0088 | 2.12 |
| IOW | VITO | 0.3068 | 6.37 |
| IOW-F2 | VITO | 0.0417 | 4.97 |
| Meta | VITO | 0.1880 | NA |
| MoBa1 | VITO | 0.1394 | 15.01 |
| MoBa2 | VITO | 0.2746 | 32.98 |
| NEST_B | VITO | -0.0166 | 3.78 |
| UKIDS | VITO | 0.1430 | 14.16 |
Figure 16: Correlation between the effect estimates of early-onset eczema EWAS, model c, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.
Figure 17: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.
Figure 18: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.
| cohort | N | N-cases | N-controls | prevalence |
|---|---|---|---|---|
| ALSPAC | 662 | 125 | 537 | 18.9 |
| CHS | 197 | 35 | 162 | 17.8 |
| EDEN | 126 | 18 | 108 | 14.3 |
| GENR | 415 | 39 | 376 | 9.4 |
| MoBa1 | 519 | 87 | 432 | 16.8 |
| MoBa2 | 237 | 61 | 176 | 25.7 |
| VITO | 66 | 11 | 55 | 16.7 |
| GOYA | 517 | 33 | 484 | 6.4 |
| IOW | 462 | 25 | 437 | 5.4 |
| Total | 3,201 | 434 | 2,767 | 13.6 |
| model | variable | beta | se | zval | pval | lower-ci | upper-ci |
|---|---|---|---|---|---|---|---|
| m3a | N | -0.00024 | 0.00027 | -0.89 | 0.37 | -0.00077 | 0.00029 |
| m3a | N_cases | -0.00027 | 0.00158 | -0.17 | 0.86 | -0.00336 | 0.00282 |
| m3a | prevalence | 0.01235 | 0.00762 | 1.62 | 0.11 | -0.00258 | 0.02728 |
| m3a | definition | -0.05261 | 0.17375 | -0.30 | 0.76 | -0.39316 | 0.28794 |
| m3a | definition_3grp | -0.08742 | 0.19265 | -0.45 | 0.65 | -0.46501 | 0.29017 |
| m3b | N | -0.00042 | 0.00024 | -1.75 | 0.08 | -0.00089 | 0.00005 |
| m3b | N_cases | -0.00140 | 0.00150 | -0.93 | 0.35 | -0.00434 | 0.00154 |
| m3b | prevalence | 0.00991 | 0.00817 | 1.21 | 0.22 | -0.00609 | 0.02592 |
| m3b | definition | -0.15004 | 0.16706 | -0.90 | 0.37 | -0.47747 | 0.17740 |
| m3b | definition_3grp | -0.22939 | 0.16026 | -1.43 | 0.15 | -0.54350 | 0.08471 |
| m3c | N | -0.00021 | 0.00033 | -0.64 | 0.52 | -0.00086 | 0.00044 |
| m3c | N_cases | -0.00133 | 0.00166 | -0.80 | 0.42 | -0.00458 | 0.00192 |
| m3c | prevalence | -0.00349 | 0.00863 | -0.40 | 0.69 | -0.02040 | 0.01342 |
| m3c | definition | -0.08385 | 0.20665 | -0.41 | 0.68 | -0.48887 | 0.32118 |
| m3c | definition_3grp | -0.16820 | 0.20856 | -0.81 | 0.42 | -0.57697 | 0.24057 |
| cohort1 | cohort2 | effect-cor | prev-diff |
|---|---|---|---|
| ALSPAC | CHS | 0.589 | 1.116 |
| ALSPAC | EDEN | 0.267 | 4.596 |
| CHS | EDEN | -0.104 | 3.481 |
| ALSPAC | GENR | 0.788 | 9.485 |
| CHS | GENR | 0.415 | 8.369 |
| EDEN | GENR | 0.115 | 4.888 |
| ALSPAC | GOYA | 0.566 | 12.499 |
| CHS | GOYA | 0.538 | 11.384 |
| EDEN | GOYA | 0.151 | 7.903 |
| GENR | GOYA | 0.457 | 3.015 |
| ALSPAC | IOW | 0.536 | 13.471 |
| CHS | IOW | 0.276 | 12.355 |
| EDEN | IOW | 0.048 | 8.874 |
| GENR | IOW | 0.533 | 3.986 |
| GOYA | IOW | 0.190 | 0.972 |
| ALSPAC | Meta | 0.932 | NA |
| CHS | Meta | 0.643 | NA |
| EDEN | Meta | 0.157 | NA |
| GENR | Meta | 0.869 | NA |
| GOYA | Meta | 0.605 | NA |
| IOW | Meta | 0.642 | NA |
| ALSPAC | MoBa1 | 0.586 | 2.119 |
| CHS | MoBa1 | 0.561 | 1.003 |
| EDEN | MoBa1 | -0.166 | 2.477 |
| GENR | MoBa1 | 0.607 | 7.365 |
| GOYA | MoBa1 | 0.241 | 10.380 |
| IOW | MoBa1 | 0.532 | 11.352 |
| Meta | MoBa1 | 0.763 | NA |
| ALSPAC | MoBa2 | 0.528 | 6.856 |
| CHS | MoBa2 | 0.410 | 7.972 |
| EDEN | MoBa2 | -0.040 | 11.453 |
| GENR | MoBa2 | 0.541 | 16.341 |
| GOYA | MoBa2 | 0.610 | 19.355 |
| IOW | MoBa2 | 0.367 | 20.327 |
| Meta | MoBa2 | 0.658 | NA |
| MoBa1 | MoBa2 | 0.427 | 8.975 |
| ALSPAC | VITO | 0.396 | 2.216 |
| CHS | VITO | 0.170 | 1.100 |
| EDEN | VITO | 0.157 | 2.381 |
| GENR | VITO | 0.415 | 7.269 |
| GOYA | VITO | 0.176 | 10.284 |
| IOW | VITO | 0.208 | 11.255 |
| Meta | VITO | 0.467 | NA |
| MoBa1 | VITO | 0.214 | 0.096 |
| MoBa2 | VITO | 0.577 | 9.072 |
Figure 19: Correlation between the effect estimates of persistent eczema EWAS, model a, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.
Figure 20: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.
Figure 21: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.
| cohort1 | cohort2 | effect-cor | prev-diff |
|---|---|---|---|
| ALSPAC | CHS | 0.5602 | 1.12 |
| ALSPAC | EDEN | 0.3047 | 4.60 |
| CHS | EDEN | -0.0056 | 3.48 |
| ALSPAC | GENR | 0.7850 | 9.48 |
| CHS | GENR | 0.4789 | 8.37 |
| EDEN | GENR | 0.2270 | 4.89 |
| ALSPAC | GOYA | 0.6058 | 12.50 |
| CHS | GOYA | 0.4684 | 11.38 |
| EDEN | GOYA | 0.3189 | 7.90 |
| GENR | GOYA | 0.4308 | 3.01 |
| ALSPAC | IOW | 0.4910 | 13.57 |
| CHS | IOW | 0.3674 | 12.46 |
| EDEN | IOW | 0.3370 | 8.98 |
| GENR | IOW | 0.5328 | 4.09 |
| GOYA | IOW | 0.2302 | 1.07 |
| ALSPAC | Meta | 0.9274 | NA |
| CHS | Meta | 0.6078 | NA |
| EDEN | Meta | 0.2931 | NA |
| GENR | Meta | 0.8478 | NA |
| GOYA | Meta | 0.6121 | NA |
| IOW | Meta | 0.6360 | NA |
| ALSPAC | MoBa1 | 0.5652 | 2.05 |
| CHS | MoBa1 | 0.5013 | 0.94 |
| EDEN | MoBa1 | -0.0313 | 2.54 |
| GENR | MoBa1 | 0.5309 | 7.43 |
| GOYA | MoBa1 | 0.2461 | 10.44 |
| IOW | MoBa1 | 0.4589 | 11.52 |
| Meta | MoBa1 | 0.7507 | NA |
| ALSPAC | MoBa2 | 0.4231 | 6.86 |
| CHS | MoBa2 | 0.0625 | 7.97 |
| EDEN | MoBa2 | 0.0586 | 11.45 |
| GENR | MoBa2 | 0.3304 | 16.34 |
| GOYA | MoBa2 | 0.4391 | 19.36 |
| IOW | MoBa2 | 0.3887 | 20.43 |
| Meta | MoBa2 | 0.5317 | NA |
| MoBa1 | MoBa2 | 0.3945 | 8.91 |
| ALSPAC | VITO | 0.3447 | 2.22 |
| CHS | VITO | -0.0691 | 1.10 |
| EDEN | VITO | -0.0578 | 2.38 |
| GENR | VITO | 0.2397 | 7.27 |
| GOYA | VITO | 0.0946 | 10.28 |
| IOW | VITO | 0.0071 | 11.36 |
| Meta | VITO | 0.3710 | NA |
| MoBa1 | VITO | 0.2426 | 0.16 |
| MoBa2 | VITO | 0.4350 | 9.07 |
Figure 22: Correlation between the effect estimates of persistent eczema EWAS, model b, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.
Figure 23: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.
Figure 24: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.
| cohort1 | cohort2 | effect-cor | prev-diff |
|---|---|---|---|
| ALSPAC | CHS | 0.789 | 18.88 |
| ALSPAC | EDEN | 0.543 | 4.60 |
| CHS | EDEN | 0.325 | 14.29 |
| ALSPAC | GENR | 0.801 | 9.48 |
| CHS | GENR | 0.590 | 9.40 |
| EDEN | GENR | 0.479 | 4.89 |
| ALSPAC | GOYA | 0.767 | 12.50 |
| CHS | GOYA | 0.611 | 6.38 |
| EDEN | GOYA | 0.316 | 7.90 |
| GENR | GOYA | 0.552 | 3.01 |
| ALSPAC | IOW | 0.583 | 13.57 |
| CHS | IOW | 0.509 | 5.31 |
| EDEN | IOW | 0.757 | 8.98 |
| GENR | IOW | 0.509 | 4.09 |
| GOYA | IOW | 0.239 | 1.07 |
| ALSPAC | Meta | 0.975 | NA |
| CHS | Meta | 0.809 | NA |
| EDEN | Meta | 0.617 | NA |
| GENR | Meta | 0.844 | NA |
| GOYA | Meta | 0.729 | NA |
| IOW | Meta | 0.703 | NA |
| ALSPAC | MoBa1 | 0.861 | 2.05 |
| CHS | MoBa1 | 0.796 | 16.83 |
| EDEN | MoBa1 | 0.548 | 2.54 |
| GENR | MoBa1 | 0.732 | 7.43 |
| GOYA | MoBa1 | 0.538 | 10.44 |
| IOW | MoBa1 | 0.671 | 11.52 |
| Meta | MoBa1 | 0.910 | NA |
| ALSPAC | MoBa2 | 0.724 | 6.86 |
| CHS | MoBa2 | 0.747 | 25.74 |
| EDEN | MoBa2 | 0.355 | 11.45 |
| GENR | MoBa2 | 0.528 | 16.34 |
| GOYA | MoBa2 | 0.669 | 19.36 |
| IOW | MoBa2 | 0.434 | 20.43 |
| Meta | MoBa2 | 0.758 | NA |
| MoBa1 | MoBa2 | 0.722 | 8.91 |
| ALSPAC | VITO | 0.274 | 2.22 |
| CHS | VITO | 0.137 | 16.67 |
| EDEN | VITO | 0.050 | 2.38 |
| GENR | VITO | 0.343 | 7.27 |
| GOYA | VITO | 0.033 | 10.28 |
| IOW | VITO | 0.184 | 11.36 |
| Meta | VITO | 0.263 | NA |
| MoBa1 | VITO | 0.080 | 0.16 |
| MoBa2 | VITO | 0.174 | 9.07 |
Figure 25: Correlation between the effect estimates of persistent eczema EWAS, model c, across different cohorts. The left-hand-side is a heatmap showing correlations between effect estimates of the top 30 CpG sites (top meaning those with the lowest P value from the meta-analysis). The right-hand-side is a heatmap showing correlations between effect estimates of all overlapping CpG sites.
Figure 26: M-statistics, prevalence and effect size These plots show calculated M-statistics, a measure of heterogeneity between studies using the top 30 CpG sites, for each study and their association with prevalence and effect size. A: the distribution of m-statistics, B: the association between M-statistics and prevalence, C: the association between M-statistics and effect size.
Figure 27: Comparison of effect sizes between cohorts The bars represent the 95% confidence intervals for the meta-analysis results.